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We introduce the notion of LImited Memory Influence Diagram (LIMID) to describe multi-stage decision problems where the traditional assumption of no forgetting is relaxed. This can be relevant in situations with multiple decision makers or when decisions must be prescribed under memory constraints, such as e.g. in partially observed Markov decision… (More)

We present a new approach to the solu tion of decision problems formulated as in fluence diagrams. The approach converts the influence diagram into a simpler structure, the Limited Memory Influence Diagram (LIMID), where only the requisite informa tion for the computation of optimal policies is depicted. Because the requisite information is explicitly… (More)

In this paper we present three different architec-tures for the evaluation of influence diagrams: HUGIN, Shafer-Shenoy (S-S), and Lazy Propagation (LP). HUGIN and LP are two new ar-chitectures introduced in this paper. The computational complexity using the three architec-tures are compared on the same structure, the LImited Memory Influence Diagram… (More)

Given a decision problem formulated in an Innuence Diagram and a strategy of the decision problem, we show how to construct a Bayesian network that enables the decision maker to evaluate the chosen strategy. We introduce the concept of a future decision, and show how the Bayesian network can be used to compute the probability of making a future decision.… (More)

We propose a novel method to obtain the N-best list of hypotheses in hidden Markov model (HMM). We show that the entire information needed to compute the N-best list from the HMM trellis graph is encapsulated in entities that can be computed in a single forward-backward iteration that usually yields the most likely state sequence. The hypotheses list can… (More)

- Steeen L Lauritzen, Dennis Nilsson
- 1999

We introduce the notion of LImited Memory Innuence Diagram (LIMID) to describe multi-stage decision problems where the traditional assumption of no forgetting is relaxed. This can be relevant in situations with multiple decision makers or when decisions must be prescribed under memory constraints, such as e.g. in partially observed Markov decision processes… (More)

- Michael Höhle, Erik Jørgensen, Dennis Nilsson
- 2000

This paper shows the applicability of LImited Memory Influence Diagrams (LIMIDs) for modeling pig production decision scenarios. Contrary to influence diagrams LIMIDs do not require remembering of previous information in multi-stage decision problems. An easy LIMID evaluation algorithm using existing software packages is presented. Furthermore , we adopt… (More)

In this paper a new tool for simultaneous optimization of decisions on multiple time scales is discussed. It combines the dynamic properties of Markov decision processes with the flexible and compact state space representation of Limited Memory Influence Diagrams (LIMIDs).

- Dennis Nilsson, Michael Höhle, Codan Forsikring
- 2005

LImited Memory Influence Diagrams (LIMIDs) are general models of decision problems for representing limited memory policies (Lauritzen and Nilsson (2001)). The evaluation of LIMIDs can be done by Single Policy Updating that produces a local maximum strategy in which no single policy modification can increase the expected utility. This paper examines the… (More)

We propose a novel method to obtain the AE-best list of hypotheses in hidden Markov model (HMM). We show that the entire information needed to compute the AE-best list from the HMM trellis graph is encapsulated in entities that can be computed in a single forward-backward iteration that usually yields the most likely state sequence. The hypotheses list can… (More)